Power calculation for two-stage case-control design

Description

This function gives power estimates for two-stage case-control design for genetic association.

Usage

tscc(model, GRR, p1, n1, n2, M, alpha.genome, pi.samples, pi.markers, K)

Arguments

Details

The false positive rates are calculated as follows,

P(|z1|>C1)P(|z2|>C2,sign(z1)=sign(z2))

and

P(|z1|>C1)P(|zj|>Cj||z1|>C1)

for replication-based and joint analyses, respectively; where C1, C2, and Cj are threshoulds at stages 1, 2 replication and joint analysis,

z1 = z(p1,p2,n1,n2,pi.samples)

z2 = z(p1,p2,n1,n2,1-pi.samples)

zj = sqrt(pi.samples)*z1+sqrt(1-pi.samples)*z2

Value

The returned value is a list containing a copy of the input plus output as follows,

model

any in c("multiplicative","additive","dominant","recessive").

GRR

genotype relative risk.

p1

the estimated risk allele frequency in cases.

pprime

expected risk allele frequency in cases.

p

expected risk allele frequency in controls.

n1

total number of cases.

n2

total number of controls.

M

total number of markers.

alpha.genome

false positive rate at genome level.

pi.samples

sample% to be genotyped at stage 1.

pi.markers

markers% to be selected (also used as the false positive rate at stage 1).

K

the population prevalence.

C

threshoulds for no stage, stage 1, stage 2, joint analysis.

power

power corresponding to C.

Note

solve.skol is adapted from CaTS.

Author(s)

Jing Hua Zhao

References

Skol AD, Scott LJ, Abecasis GR, Boehkne M (2006). Joint analysis in more efficient than replication-based aalysis for two-stage genome-wide association studies. Nature Genetics 38:209-213

Examples

## Not run: 
K <- 0.1
p1 <- 0.4
n1 <- 1000
n2 <- 1000 
M <- 300000
alpha.genome <- 0.05
GRR <- 1.4
p1 <- 0.4
pi.samples <- 0.2
pi.markers <- 0.1

options(echo=FALSE)
cat("sample\
for(GRR in c(1.3,1.35,1.40))
{
   cat("\n")
   for(pi.samples in c(1.0,0.5,0.4,0.3,0.2))
   {
      if(pi.samples==1.0) s <- 1.0
      else s <- c(0.1,0.05,0.01)
      for(pi.markers in s)
      {
        x <- tscc("multiplicative",GRR,p1,n1,n2,M,alpha.genome,
                  pi.samples,pi.markers,K)
        l <- c(pi.samples,pi.markers,GRR,x$C,x$power)
        l <- sprintf("\
                     l[1],l[2],l[3],l[4],l[5],l[6],l[7],l[8],l[9],l[10],l[11])
        cat(l,"\n")
      }
      cat("\n")
   }
}
options(echo=TRUE)

## End(Not run)